Learning a General Model: Folding Clothing with Topological Dynamics
This addresses the problem of robotic clothing manipulation for applications like domestic assistance, but it is incremental as it builds on existing methods like GNNs and semantic segmentation.
The paper tackles the challenge of manipulating garments with high degrees of freedom by proposing a general topological dynamics model to fold complex clothing, achieving effective recognition and folding of jackets with self-occlusion in experiments.
The high degrees of freedom and complex structure of garments present significant challenges for clothing manipulation. In this paper, we propose a general topological dynamics model to fold complex clothing. By utilizing the visible folding structure as the topological skeleton, we design a novel topological graph to represent the clothing state. This topological graph is low-dimensional and applied for complex clothing in various folding states. It indicates the constraints of clothing and enables predictions regarding clothing movement. To extract graphs from self-occlusion, we apply semantic segmentation to analyze the occlusion relationships and decompose the clothing structure. The decomposed structure is then combined with keypoint detection to generate the topological graph. To analyze the behavior of the topological graph, we employ an improved Graph Neural Network (GNN) to learn the general dynamics. The GNN model can predict the deformation of clothing and is employed to calculate the deformation Jacobi matrix for control. Experiments using jackets validate the algorithm's effectiveness to recognize and fold complex clothing with self-occlusion.